The benefits of different levels of engagement with test, trace and isolate procedures are investigated for a pandemic in which there is little population immunity, in terms of productivity and public health. Simple mathematical modelling is used in the context of a single, relatively closed workplace such as a factory or back-office where, in normal operation, each worker has lengthy interactions with a fixed set of colleagues. A discrete-time SEIR model on a fixed interaction graph is simulated with parameters that are motivated by the recent COVID-19 pandemic in the UK during a post-peak phase, including a small risk of viral infection from outside the working environment. Two kinds of worker are assumed, transparents who regularly test, share their results with colleagues and isolate as soon as a contact tests positive for the disease, and opaques who do none of these. Moreover, the simulations are constructed as a ‘playable model’ in which the transparency level, disease parameters and mean interaction degree can be varied by the user. The model is also analysed in the continuum limit. All simulations point to the double benefit of transparency in both maximizing productivity and minimizing overall infection rates. Based on these findings, public policy implications are discussed for how to incentivise this mutually beneficial behaviour in different kinds of workplace, and simple recommendations are made.

中文翻译：

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大流行封锁后，同行透明度对安全工作场所运营的好处

针对人群免疫力低下的大流行，研究了不同程度参与测试、追踪和隔离程序在生产力和公共卫生方面的好处。简单的数学模型用于单一的、相对封闭的工作场所，例如工厂或后台，在正常操作中，每个工人与一组固定的同事进行长时间的互动。固定交互图上的离散时间 SEIR 模型使用受最近英国 COVID-19 大流行高峰后阶段影响的参数进行模拟，其中包括来自工作环境之外的病毒感染的小风险。假设有两种工作人员，一种是定期检测、与同事分享结果并在接触者检测呈阳性后立即隔离的透明人员，另一种是不做这些工作的不透明人员。此外，模拟被构建为“可玩模型”，其中透明度水平、疾病参数和平均交互程度可以由用户改变。该模型还在连续极限下进行了分析。所有模拟都表明透明度在最大化生产力和最小化总体感染率方面具有双重好处。基于这些发现，讨论了如何在不同类型的工作场所激励这种互利行为的公共政策影响，并提出了简单的建议。